Abstract. Many organisations (government of non-government) use websites to share information of new recruitment for the workers. This information overflows on thousands of sites with various attributes and criteria. However, this availability forms a complex puzzle in the selection process and lead to inefficient runtime. This study proposes a simple method for job searching simplification through a construction and collaboration of web scraping technique and classification using Naïve Bayes on search engine. This study is resulting an effective and efficient application for users to seek a potential job that fit in with their interests.
United Nations Educational, Scientific, and Culture Organization (UNESCO) has recognized batik cloth is one of the world cultural heritage that originated from Indonesia, exactly on October 2, 2009. Batik in Indonesia has a motive that many, varied and almost every motive of batik various regions have similar motives, but if viewed in more detail batik cloth from different regions are not the same. Certain people who have expertise and knowledge in the field of batik that can distinguish batik motive from various regions. Lampung is one area in Indonesia that has a cloth motive that characterizes the Lampung area used as batik cloth. This study discusses the backpropagation artificial neural network that will be used for the classification of pattern batik motive Lampung. Batik motive lampung used is sembagi, siger ratu agung, jung agung and siger clove cengkih, while for batik is not a motive Lampung used parang kusumo and broken parang. Stages to be done are scaling, grayscale, tresholding and classification. Comparison of training data and data testing used is 70:30 and 80:30 with the need of backpropagation neural network that is epoch = 2000, learning rate = 0.1 and target error = 0.001. The greatest accuracy value is found in the 70:30 data is 92%.
Gita Persada Butterfly Park is the only breeding of engineered in situ butterflies in Indonesia. It is located in Lampung and has approximately 211 species of breeding butterflies. Each species of Butterflies has a different texture on its wings. The Limited ability of the human eye to distinguishing typical textures on butterfly species is the reason for conducting a research on butterfly identification based on pattern recognition. The dataset consists of 600 images of butterfly’s upper wing from six species: Centhosia penthesilea, Papilio memnon, Papilio nephelus, Pachliopta aristolochiae, Papilio peranthus and Troides helena. The pre-processing stage is conducted using scaling, segmentation and grayscale methods. The GLCM method is used to recognize the characteristics of butterfly images using pixel distance and Angular direction 0o, 45o, 90o and 135o. The features used is angular second moment, contrast, homogeneity and correlation. KNN classification method in this study uses k values1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 based on the Rule of Thumb. The result of this study indicate that Centhosia penthesilea and Papilio nephelus classes can be classified properly compared to the other 4 classes and require a classification time of 2 seconds at each angular orientation. The highest accuracy is 91.1% with a value of in the angle of 90o and error rate8.9%. Classification error occured because the value of the test data features is more dominant with the value of the training image features in different classes than the supposed class. Another reason is because of imperfect test data.
Livestock is a source of animal protein that contains essential acids that improve human intelligence and health. Popular livestock in Indonesia is cow. Consumption of meat per capita is increased by 0.1% kg / capita / year. The high demand for beef in Indonesia is due to the increasing of population in Indonesia by 1.49% per year. More than 90% of cows are reared by rural communities with less of knowledge about livestock and have low economic capabilities. In addition, the number of experts or veterinarians are also limited. One of the solutions that can be done to socialize the knowledge of experts or veterinarians is by using expert system. Some methods that can be used in expert systems are Bayesian network and Dempster-Shafer method. The purpose of this research is to analyze the comparison of cow disease diagnosis with bayesian network and Dempster-Shafer method. In order to know which method is better in diagnosing cow disease. The data used is 21 cow diseases with 77 symptoms. Each method is tested with the same 10 cases. The conclusions obtained by Bayesian network and Dempster-Shafer method. Both of methods give the same diagnosis results but with different percentage. The mean value of diagnosis percentage by Dempster-Shafer method is 87,2% while bayesian network method is 75,3%. Thus, it can be said that the Dempster-Shafer method is better at diagnosing cow disease.
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